Server, method, and computer-readable storage medium for selecting eyewear equipment

Abstract

A server includes processing circuitry configured to receive one or more images, the one or more images including one or more representations of people. Additionally, the processing circuitry is configured to apply a neural network to the one or more images, wherein the neural network classifies at least one aesthetic component of each image of the one or more images, an aesthetic component score being generated for each image in the one or more images. Further, the processing circuitry is configured to generate a user eyewear equipment profile for a user, the user being matched to a persona from a personae database, each persona in the personae database being linked to one or more persona eyewear equipment profiles, the one or more persona eyewear equipment profiles being based on the aesthetic component score, and select eyewear equipment for the user based on the generated user eyewear equipment profile.

The invention claimed is: 
     
       1. A server, comprising:
 processing circuitry configured to
 receive one or more images, the one or more images including one or more representations of people, 
 apply a neural network to the one or more images, wherein the neural network classifies at least one aesthetic component of each image of the one or more images, an aesthetic component score being generated for each image in the one or more images, 
 generate a user eyewear equipment profile for a user, the user being matched to a persona from a personae database, each persona in the personae database being linked to one or more persona eyewear equipment profiles, the one or more persona eyewear equipment profiles being based on the aesthetic component score, and 
 select eyewear equipment for the user based on the generated user eyewear equipment profile, 
 
 wherein the aesthetic component score is calculated based on a number of occurrences of an item including a type, a color, a shape, a product, a pattern, a brand, or a product category. 
 
     
     
       2. The server of  claim 1 , wherein the processing circuitry is further configured to
 sum a number of appearances of each classified aesthetic component in the one or more images, wherein the number of appearances is weighed as a function of predetermined criteria including one or more of a time when an image the item appears in was released, a time period when all the plurality of images including the item were released, a person that provided the image, and feedback of other people regarding the image including social media engagement, and 
 calculate the aesthetic component score for each of the one or more images based on how frequently each aesthetic component appeared in the one or more images, 
 wherein each aesthetic component is weighted based on one or more predetermined criteria and each aesthetic component in each of the one or more images is combined to calculate aesthetic component statistics for the one or more images. 
 
     
     
       3. The server of  claim 2 , wherein the processing circuitry is further configured to
 determine whether the eyewear equipment selection for the user is based on a synthesis of all aesthetic components in the one or more images, 
 output global statistics in response to a determination that the eyewear equipment selection for the user is based on the attribute of all aesthetic components in the one or more images, the global statistics being based on the aesthetic component score, 
 determine whether the eyewear equipment selection for the user is based on eyewear aesthetic components in the one or more images, and 
 output eyewear statistics in response to a determination that the eyewear equipment selection for the user is based on eyewear aesthetic components in the one or more images, the eyewear statistics being based on the aesthetic component score, 
 wherein the aesthetic component score is only incorporated into the global statistics and the eyewear statistics when the aesthetic component score is greater than a predetermined minimum aesthetic component score. 
 
     
     
       4. The server of  claim 1 , wherein in response to the received one or more images being from publically available media corresponding to a panel of pre-selected celebrities or trend setters, the processing circuitry is further configured to
 generate the personae database by defining or computing a set of personae, each persona in the personae database having a human typical profile, the human typical profile being based on physical and context characteristics of the people in the one or more images, 
 associate each pre-selected celebrity or trend setter with a matching persona in the personae database, the association being based on physical and context characteristics of the pre-selected celebrity or trend setter, and 
 associate eyewear equipment to each persona in the set of personae based on the aesthetic component score of the eyewear equipment of the pre-selected celebrities or trend setters, the associated eyewear equipment to each persona corresponding to the one or more persona eyewear equipment profiles. 
 
     
     
       5. The server of  claim 4 , wherein in response to the received one or more images being one or more images of the user, the processing circuitry is further configured to
 generate a profile for the user based on the physical and context characteristics of the user, 
 associate the user to the matching persona in the personae database that most closely matches the profile generated for the user based on the physical and context characteristics of the user, and 
 associate the user to the user eyewear equipment profile based on the matching persona, the user eyewear equipment profile being based on the one or more persona eyewear equipment profiles of the matching persona. 
 
     
     
       6. A method for selecting eyewear equipment, comprising:
 receiving, by processing circuitry, one or more images, the one or more images including one or more representations of people; 
 applying, by the processing circuitry, a neural network to the one or more images, wherein the neural network classifies at least one aesthetic component of each image of the one or more images, an aesthetic component score being generated for each image in the one or more images; 
 generating, by the processing circuitry, a user eyewear equipment profile for a user, the user being matched to a persona from a personae database, each persona in the personae database being linked to one or more persona eyewear equipment profiles, the one or more persona eyewear equipment profiles being based on the aesthetic component score; and 
 selecting, by the processing circuitry, eyewear equipment for the user based on the generated user eyewear equipment profile, 
 wherein the aesthetic component score is calculated based on a number of occurrences of an item including a type, a color, a shape, a product, a pattern, a brand, or a product category. 
 
     
     
       7. The method of  claim 6 , further comprising:
 summing a number of appearances of each classified aesthetic component in the one or more images, wherein the number of appearances is weighed as a function of predeteimined criteria including one or more of a time when an image the item appears in was released, a time period when all the plurality of images including the item were released, a person that provided the image, and feedback of other people regarding the image including social media engagement; and 
 calculating the aesthetic component score for each of the one or more images based on how frequently each aesthetic component appeared in the one or more images, 
 wherein each aesthetic component is weighted based on one or more predetermined criteria and each aesthetic component in each of the one or more images is combined to calculate aesthetic component statistics for the one or more images. 
 
     
     
       8. The method of  claim 7 , further comprising:
 determining whether the eyewear equipment selection for the user is based on a synthesis of all aesthetic components in the one or more images; 
 outputting global statistics in response to a determination that the eyewear equipment selection for the user is based on the attribute of all aesthetic components in the one or more images, the global statistics being based on the aesthetic component score; 
 determining whether the eyewear equipment selection for the user is based on eyewear aesthetic components in the one or more images; and 
 outputting eyewear statistics in response to a determination that the eyewear equipment selection for the user is based on eyewear aesthetic components in the one or more images, the eyewear statistics being based on the aesthetic component score, 
 wherein the aesthetic component score is only incorporated into the global statistics and the eyewear statistics when the aesthetic component score is greater than a predetermined minimum aesthetic component score. 
 
     
     
       9. The method of  claim 6 , in response to the received one or more images being from publically available media corresponding to a panel of pre-selected celebrities or trend setters, further comprising:
 generating the personae database by defining or computing a set of personae, each persona in the personae database having a human typical profile, the human typical profile being based on physical and context characteristics of the people in the one or more images; 
 associating each pre-selected celebrity or trend setter with a matching persona in the personae database, the association being based on physical and context characteristics of the pre-selected celebrity or trend setter; and 
 associating eyewear equipment to each persona in the set of personae based on the aesthetic component score of the eyewear equipment of the pre-selected celebrities or trend setters, the associated eyewear equipment to each persona corresponding to the one or more persona eyewear equipment profile. 
 
     
     
       10. The method of  claim 9 , in response to the received one or more images being one or more images of the user, further comprising:
 generating a profile for the user based on the physical and context characteristics of the user; 
 associating the user to the matching persona in the personae database that most closely matches the profile generated for the user based on the physical and context characteristics of the user; and 
 associating the user to the user eyewear equipment profile based on the matching persona, the user eyewear equipment profile being based on the one or more persona eyewear equipment profiles of the matching persona. 
 
     
     
       11. A non-transitory computer-readable storage medium storing computer-readable instructions thereon which, when executed by a computer, cause the computer to perform a method, the method comprising:
 receiving one or more images, the one or more images including one or more representations of people, 
 applying a neural network to the one or more images, wherein the neural network classifies at least one aesthetic component of each image of the one or more images, an aesthetic component score being generated for each image in the one or more images, 
 generating a user eyewear equipment profile for a user, the user being matched to a persona from a personae database, each persona in the personae database being linked to one or more persona eyewear equipment profiles, the one or more persona eyewear equipment profiles being based on the aesthetic component score, and 
 selecting eyewear equipment for the user based on the generated user eyewear equipment profile, 
 wherein the aesthetic component score is calculated based on a number of occurrences of an item including a type, a color, a shape, a product, a pattern, a brand, or a product category. 
 
     
     
       12. The non-transitory computer-readable storage medium of  claim 11 , further comprising:
 summing a number of appearances of each classified aesthetic component in the one or more images, wherein the number of appearances is weighed as a function of predeteimined criteria including one or more of a time when an image the item appears in was released, a time period when all the plurality of images including the item were released, a person that provided the image, and feedback of other people regarding the image including social media engagement; and 
 calculating the aesthetic component score for each of the one or more images based on how frequently each aesthetic component appeared in the one or more images, 
 wherein each aesthetic component is weighted based on one or more predetermined criteria and each aesthetic component in each of the one or more images is combined to calculate aesthetic component statistics for the one or more images. 
 
     
     
       13. The non-transitory computer-readable storage medium of  claim 12 , further comprising:
 determining whether the eyewear equipment selection for the user is based on a synthesis of all aesthetic components in the one or more images; 
 outputting global statistics in response to a determination that the eyewear equipment selection for the user is based on the attribute of all aesthetic components in the one or more images, the global statistics being based on the aesthetic component score; 
 determining whether the eyewear equipment selection for the user is based on eyewear aesthetic components in the one or more images; and 
 outputting eyewear statistics in response to a determination that the eyewear equipment selection for the user is based on eyewear aesthetic components in the one or more images, the eyewear statistics being based on the aesthetic component score, 
 wherein the aesthetic component score is only incorporated into the global statistics and the eyewear statistics when the aesthetic component score is greater than a predetermined minimum aesthetic component score. 
 
     
     
       14. The non-transitory computer-readable storage medium of  claim 11 , in response to the received one or more images being from publically available media corresponding to a panel of pre-selected celebrities or trend setters, further comprising:
 generating the personae database by defining or computing a set of personae, each persona in the personae database having a human typical profile, the human typical profile being based on physical and context characteristics of the people in the one or more images; 
 associating each pre-selected celebrity or trend setter with a matching persona in the personae database, the association being based on physical and context characteristics of the pre-selected celebrity or trend setter; and 
 associating eyewear equipment to each persona in the set of personae based on the aesthetic component score of the eyewear equipment of the pre-selected celebrities or trend setters, the associated eyewear equipment to each persona corresponding to the one or more persona eyewear equipment profiles. 
 
     
     
       15. The non-transitory computer-readable storage medium of  claim 14 , in response to the received one or more images being one or more images of the user, further comprising:
 generating a profile for the user based on the physical and context characteristics of the user; 
 associating the user to the matching persona in the personae database that most closely matches the profile generated for the user based on the physical and context characteristics of the user; and 
 associating the user to the user eyewear equipment profile based on the matching persona, the user eyewear equipment profile being based on the one or more persona eyewear equipment profiles of the matching persona.